

import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class 					__lowercase  :





									"""simple docstring"""

									@staticmethod
									def       snake_case			(					*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       str:
																pass







@is_pipeline_test
@require_vision
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							@require_torch
							def       snake_case			(					self						)		->       int:
														A      :					str      		=					pipeline(
														    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''    ,						)
														A      :					Union[str, Any]      		=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
														A      :					int      		=					image_classifier(__UpperCAmelCase    ,						candidate_labels=['''a''', '''b''', '''c''']						)

														# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
														# python and torch versions.
														self.assertIn(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
														        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
														    ]    ,						)

														A      :					List[str]      		=					image_classifier([image] * 5    ,						candidate_labels=['''A''', '''B''', '''C''']    ,						batch_size=2						)
														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														    ]    ,						)




							@require_tf
							def       snake_case			(					self						)		->       Any:
														A      :					Union[str, Any]      		=					pipeline(
														    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''    ,						framework='''tf'''						)
														A      :					str      		=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
														A      :					Any      		=					image_classifier(__UpperCAmelCase    ,						candidate_labels=['''a''', '''b''', '''c''']						)

														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]    ,						)

														A      :					Dict      		=					image_classifier([image] * 5    ,						candidate_labels=['''A''', '''B''', '''C''']    ,						batch_size=2						)
														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														        [
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														            {'''score''': 0.3_3_3, '''label''': ANY(__UpperCAmelCase						)},
														        ],
														    ]    ,						)




							@slow
							@require_torch
							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Any      		=					pipeline(
														    task='''zero-shot-image-classification'''    ,						model='''openai/clip-vit-base-patch32'''    ,						)
														# This is an image of 2 cats with remotes and no planes
														A      :					Any      		=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
														A      :					Tuple      		=					image_classifier(__UpperCAmelCase    ,						candidate_labels=['''cat''', '''plane''', '''remote''']						)

														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        {'''score''': 0.5_1_1, '''label''': '''remote'''},
														        {'''score''': 0.4_8_5, '''label''': '''cat'''},
														        {'''score''': 0.0_0_4, '''label''': '''plane'''},
														    ]    ,						)

														A      :					Any      		=					image_classifier([image] * 5    ,						candidate_labels=['''cat''', '''plane''', '''remote''']    ,						batch_size=2						)
														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        [
														            {'''score''': 0.5_1_1, '''label''': '''remote'''},
														            {'''score''': 0.4_8_5, '''label''': '''cat'''},
														            {'''score''': 0.0_0_4, '''label''': '''plane'''},
														        ],
														    ]
														    * 5    ,						)






							@slow
							@require_tf
							def       snake_case			(					self						)		->       Dict:
														A      :					Any      		=					pipeline(
														    task='''zero-shot-image-classification'''    ,						model='''openai/clip-vit-base-patch32'''    ,						framework='''tf'''						)
														# This is an image of 2 cats with remotes and no planes
														A      :					List[str]      		=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
														A      :					Optional[int]      		=					image_classifier(__UpperCAmelCase    ,						candidate_labels=['''cat''', '''plane''', '''remote''']						)
														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        {'''score''': 0.5_1_1, '''label''': '''remote'''},
														        {'''score''': 0.4_8_5, '''label''': '''cat'''},
														        {'''score''': 0.0_0_4, '''label''': '''plane'''},
														    ]    ,						)

														A      :					int      		=					image_classifier([image] * 5    ,						candidate_labels=['''cat''', '''plane''', '''remote''']    ,						batch_size=2						)
														self.assertEqual(
														    nested_simplify(__UpperCAmelCase						)    ,						[
														        [
														            {'''score''': 0.5_1_1, '''label''': '''remote'''},
														            {'''score''': 0.4_8_5, '''label''': '''cat'''},
														            {'''score''': 0.0_0_4, '''label''': '''plane'''},
														        ],
														    ]
														    * 5    ,						)









import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														self.register_modules(vqvae=__UpperCAmelCase    ,						unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						**__UpperCAmelCase    ,						)		->       Union[Tuple, ImagePipelineOutput]:
														A      :					List[Any]      		=					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						generator=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					latents.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					Optional[int]      		=					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__UpperCAmelCase						)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														A      :					str      		=					'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

														A      :					List[str]      		=					{}
														if accepts_eta:
																					A      :					Optional[Any]      		=					eta

														for t in self.progress_bar(self.scheduler.timesteps						):
																					A      :					Any      		=					self.scheduler.scale_model_input(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					# predict the noise residual
																					A      :					Dict      		=					self.unet(__UpperCAmelCase    ,						__UpperCAmelCase						).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					int      		=					self.scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						).prev_sample

														# decode the image latents with the VAE
														A      :					str      		=					self.vqvae.decode(__UpperCAmelCase						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)
														A      :					int      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
														if output_type == "pil":
																					A      :					Union[str, Any]      		=					self.numpy_to_pil(__UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__UpperCAmelCase						)






